
"""simple docstring"""


from collections import deque



class 	__a	:






       def __init__(	self			:				List[str]							,   UpperCAmelCase_			:				str							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				int      )->	None:







              """simple docstring"""
              UpperCamelCase       			=		process_name  # process name
              UpperCamelCase       			=		arrival_time  # arrival time of the process
              # completion time of finished process or last interrupted time
              UpperCamelCase       			=		arrival_time
              UpperCamelCase       			=		burst_time  # remaining burst time
              UpperCamelCase       			=		0  # total time of the process wait in ready queue
              UpperCamelCase       			=		0  # time from arrival time to completion time





class 	__a	:






       def __init__(	self			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				list[int]							,   UpperCAmelCase_			:				deque[Process]							,   UpperCAmelCase_			:				int							,   )->	None:







              """simple docstring"""
              # total number of mlfq's queues
              UpperCamelCase       			=		number_of_queues
              # time slice of queues that round robin algorithm applied
              UpperCamelCase       			=		time_slices
              # unfinished process is in this ready_queue
              UpperCamelCase       			=		queue
              # current time
              UpperCamelCase       			=		current_time
              # finished process is in this sequence queue
              UpperCamelCase       			=		deque()






       def 						_SCREAMING_SNAKE_CASE							(	self			:				int      )->	list[str]:







              """simple docstring"""
              UpperCamelCase       			=		[]
              for i in range(len(self.finish_queue      )      ):
                     sequence.append(self.finish_queue[i].process_name      )
              return sequence






       def 						_SCREAMING_SNAKE_CASE							(	self			:				str							,   UpperCAmelCase_			:				list[Process]      )->	list[int]:







              """simple docstring"""
              UpperCamelCase       			=		[]
              for i in range(len(UpperCAmelCase_      )      ):
                     waiting_times.append(queue[i].waiting_time      )
              return waiting_times






       def 						_SCREAMING_SNAKE_CASE							(	self			:				int							,   UpperCAmelCase_			:				list[Process]      )->	list[int]:







              """simple docstring"""
              UpperCamelCase       			=		[]
              for i in range(len(UpperCAmelCase_      )      ):
                     turnaround_times.append(queue[i].turnaround_time      )
              return turnaround_times






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]							,   UpperCAmelCase_			:				list[Process]      )->	list[int]:







              """simple docstring"""
              UpperCamelCase       			=		[]
              for i in range(len(UpperCAmelCase_      )      ):
                     completion_times.append(queue[i].stop_time      )
              return completion_times






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]							,   UpperCAmelCase_			:				deque[Process]      )->	list[int]:







              """simple docstring"""
              return [q.burst_time for q in queue]






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple							,   UpperCAmelCase_			:				Process      )->	int:







              """simple docstring"""
              process.waiting_time += self.current_time - process.stop_time
              return process.waiting_time






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple							,   UpperCAmelCase_			:				deque[Process]      )->	deque[Process]:







              """simple docstring"""
              UpperCamelCase       			=		deque()  # sequence deque of finished process
              while len(UpperCAmelCase_      ) != 0:
                     UpperCamelCase       			=		ready_queue.popleft()  # current process

                     # if process's arrival time is later than current time, update current time
                     if self.current_time < cp.arrival_time:
                            self.current_time += cp.arrival_time

                     # update waiting time of current process
                     self.update_waiting_time(UpperCAmelCase_      )
                     # update current time
                     self.current_time += cp.burst_time
                     # finish the process and set the process's burst-time 0
                     UpperCamelCase       			=		0
                     # set the process's turnaround time because it is finished
                     UpperCamelCase       			=		self.current_time - cp.arrival_time
                     # set the completion time
                     UpperCamelCase       			=		self.current_time
                     # add the process to queue that has finished queue
                     finished.append(UpperCAmelCase_      )

              self.finish_queue.extend(UpperCAmelCase_      )  # add finished process to finish queue
              # FCFS will finish all remaining processes
              return finished






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Dict							,   UpperCAmelCase_			:				deque[Process]							,   UpperCAmelCase_			:				int      )->	tuple[deque[Process], deque[Process]]:







              """simple docstring"""
              UpperCamelCase       			=		deque()  # sequence deque of terminated process
              # just for 1 cycle and unfinished processes will go back to queue
              for _ in range(len(UpperCAmelCase_      )      ):
                     UpperCamelCase       			=		ready_queue.popleft()  # current process

                     # if process's arrival time is later than current time, update current time
                     if self.current_time < cp.arrival_time:
                            self.current_time += cp.arrival_time

                     # update waiting time of unfinished processes
                     self.update_waiting_time(UpperCAmelCase_      )
                     # if the burst time of process is bigger than time-slice
                     if cp.burst_time > time_slice:
                            # use CPU for only time-slice
                            self.current_time += time_slice
                            # update remaining burst time
                            cp.burst_time -= time_slice
                            # update end point time
                            UpperCamelCase       			=		self.current_time
                            # locate the process behind the queue because it is not finished
                            ready_queue.append(UpperCAmelCase_      )
                     else:
                            # use CPU for remaining burst time
                            self.current_time += cp.burst_time
                            # set burst time 0 because the process is finished
                            UpperCamelCase       			=		0
                            # set the finish time
                            UpperCamelCase       			=		self.current_time
                            # update the process' turnaround time because it is finished
                            UpperCamelCase       			=		self.current_time - cp.arrival_time
                            # add the process to queue that has finished queue
                            finished.append(UpperCAmelCase_      )

              self.finish_queue.extend(UpperCAmelCase_      )  # add finished process to finish queue
              # return finished processes queue and remaining processes queue
              return finished, ready_queue






       def 						_SCREAMING_SNAKE_CASE							(	self			:				int      )->	deque[Process]:







              """simple docstring"""

              #  all queues except last one have round_robin algorithm
              for i in range(self.number_of_queues - 1      ):
                     UpperCamelCase			,							UpperCamelCase       			=		self.round_robin(
                         self.ready_queue							,   self.time_slices[i]      )
              #  the last queue has first_come_first_served algorithm
              self.first_come_first_served(self.ready_queue      )

              return self.finish_queue


if __name__ == "__main__":
      import doctest

      SCREAMING_SNAKE_CASE			  =  Process("""P1""", 0, 53)
      SCREAMING_SNAKE_CASE			  =  Process("""P2""", 0, 17)
      SCREAMING_SNAKE_CASE			  =  Process("""P3""", 0, 68)
      SCREAMING_SNAKE_CASE			  =  Process("""P4""", 0, 24)
      SCREAMING_SNAKE_CASE			  =  3
      SCREAMING_SNAKE_CASE			  =  [17, 25]
      SCREAMING_SNAKE_CASE			  =  deque([Pa, Pa, Pa, Pa])

      if len(time_slices) != number_of_queues - 1:
            raise SystemExit(0)

      doctest.testmod(extraglobs={"""queue""": deque([Pa, Pa, Pa, Pa])})

      SCREAMING_SNAKE_CASE			  =  Process("""P1""", 0, 53)
      SCREAMING_SNAKE_CASE			  =  Process("""P2""", 0, 17)
      SCREAMING_SNAKE_CASE			  =  Process("""P3""", 0, 68)
      SCREAMING_SNAKE_CASE			  =  Process("""P4""", 0, 24)
      SCREAMING_SNAKE_CASE			  =  3
      SCREAMING_SNAKE_CASE			  =  [17, 25]
      SCREAMING_SNAKE_CASE			  =  deque([Pa, Pa, Pa, Pa])
      SCREAMING_SNAKE_CASE			  =  MLFQ(number_of_queues, time_slices, queue, 0)
      SCREAMING_SNAKE_CASE			  =  mlfq.multi_level_feedback_queue()

      # print total waiting times of processes(P1, P2, P3, P4)
      print(
          F'''waiting time:\
        \t\t\t{MLFQ.calculate_waiting_time(mlfq, [Pa, Pa, Pa, Pa])}'''
      )
      # print completion times of processes(P1, P2, P3, P4)
      print(
          F'''completion time:\
        \t\t{MLFQ.calculate_completion_time(mlfq, [Pa, Pa, Pa, Pa])}'''
      )
      # print total turnaround times of processes(P1, P2, P3, P4)
      print(
          F'''turnaround time:\
        \t\t{MLFQ.calculate_turnaround_time(mlfq, [Pa, Pa, Pa, Pa])}'''
      )
      # print sequence of finished processes
      print(
          F'''sequence of finished processes:\
        {mlfq.calculate_sequence_of_finish_queue()}'''
      )







"""simple docstring"""


from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
      import datasets



class 	__a	(   _lowerCAmelCase						):






       @slow
       @require_torch
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	Dict:







              """simple docstring"""
              UpperCamelCase       			=		EncoderDecoderModel.from_encoder_decoder_pretrained("prajjwal1/bert-tiny"							,   "prajjwal1/bert-tiny"      )
              UpperCamelCase       			=		BertTokenizer.from_pretrained("bert-base-uncased"      )

              UpperCamelCase       			=		bertabert.config.encoder.vocab_size
              UpperCamelCase       			=		tokenizer.sep_token_id
              UpperCamelCase       			=		tokenizer.cls_token_id
              UpperCamelCase       			=		128

              UpperCamelCase       			=		datasets.load_dataset("cnn_dailymail"							,   "3.0.0"							,   split="train[:1%]"      )
              UpperCamelCase       			=		datasets.load_dataset("cnn_dailymail"							,   "3.0.0"							,   split="validation[:1%]"      )

              UpperCamelCase       			=		train_dataset.select(range(32      )      )
              UpperCamelCase       			=		val_dataset.select(range(16      )      )

              UpperCamelCase       			=		4

              def _map_to_encoder_decoder_inputs(UpperCAmelCase_			:				Any      ):
                     # Tokenizer will automatically set [BOS] <text> [EOS]
                     UpperCamelCase       			=		tokenizer(batch["article"]							,   padding="max_length"							,   truncation=UpperCAmelCase_							,   max_length=512      )
                     UpperCamelCase       			=		tokenizer(batch["highlights"]							,   padding="max_length"							,   truncation=UpperCAmelCase_							,   max_length=128      )
                     UpperCamelCase       			=		inputs.input_ids
                     UpperCamelCase       			=		inputs.attention_mask

                     UpperCamelCase       			=		outputs.input_ids
                     UpperCamelCase       			=		outputs.input_ids.copy()
                     UpperCamelCase       			=		[
                         [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
                     ]
                     UpperCamelCase       			=		outputs.attention_mask

                     assert all(len(UpperCAmelCase_      ) == 512 for x in inputs.input_ids      )
                     assert all(len(UpperCAmelCase_      ) == 128 for x in outputs.input_ids      )

                     return batch

              def _compute_metrics(UpperCAmelCase_			:				Any      ):
                     UpperCamelCase       			=		pred.label_ids
                     UpperCamelCase       			=		pred.predictions

                     # all unnecessary tokens are removed
                     UpperCamelCase       			=		tokenizer.batch_decode(UpperCAmelCase_							,   skip_special_tokens=UpperCAmelCase_      )
                     UpperCamelCase       			=		tokenizer.batch_decode(UpperCAmelCase_							,   skip_special_tokens=UpperCAmelCase_      )

                     UpperCamelCase       			=		sum([int(pred_str[i] == label_str[i]      ) for i in range(len(UpperCAmelCase_      )      )]      ) / len(UpperCAmelCase_      )

                     return {"accuracy": accuracy}

              # map train dataset
              UpperCamelCase       			=		train_dataset.map(
                  _map_to_encoder_decoder_inputs							,   batched=UpperCAmelCase_							,   batch_size=UpperCAmelCase_							,   remove_columns=["article", "highlights"]							,   )
              train_dataset.set_format(
                  type="torch"							,   columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"]							,   )

              # same for validation dataset
              UpperCamelCase       			=		val_dataset.map(
                  _map_to_encoder_decoder_inputs							,   batched=UpperCAmelCase_							,   batch_size=UpperCAmelCase_							,   remove_columns=["article", "highlights"]							,   )
              val_dataset.set_format(
                  type="torch"							,   columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"]							,   )

              UpperCamelCase       			=		self.get_auto_remove_tmp_dir()

              UpperCamelCase       			=		SeqaSeqTrainingArguments(
                  output_dir=UpperCAmelCase_							,   per_device_train_batch_size=UpperCAmelCase_							,   per_device_eval_batch_size=UpperCAmelCase_							,   predict_with_generate=UpperCAmelCase_							,   evaluation_strategy="steps"							,   do_train=UpperCAmelCase_							,   do_eval=UpperCAmelCase_							,   warmup_steps=0							,   eval_steps=2							,   logging_steps=2							,   )

              # instantiate trainer
              UpperCamelCase       			=		SeqaSeqTrainer(
                  model=UpperCAmelCase_							,   args=UpperCAmelCase_							,   compute_metrics=_compute_metrics							,   train_dataset=UpperCAmelCase_							,   eval_dataset=UpperCAmelCase_							,   tokenizer=UpperCAmelCase_							,   )

              # start training
              trainer.train()





